Automatically-generated Concept Maps as a Learning Tool

Authors: Xia Lin

Abstract:

Concept maps can be generated manually or automatically. It is important to recognize differences of the two types of concept maps. The automatically generated concept maps are dynamic, interactive, and full of associations between the terms on the maps and the underlying documents. Through a specific concept mapping system, Visual Concept Explorer (VCE), this paper discusses how automatically generated concept maps are different from manually generated concept maps and how different applications and learning opportunities might be created with the automatically generated concept maps. The paper presents several examples of learning strategies that take advantages of the automatically generated concept maps for concept learning and exploration.

Keywords: Concept maps, Dynamic concept representation, learning strategies, visual interface, Visual Concept Explorer.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1333176

References:

[1] Novak, J. D. & Ca├▒as, A. J. (2008). The Theory Underlying Concept Maps and How to Construct and Use Them, Technical Report IHMC CmapTools 2006-01 Rev 01-2008, Florida Institute for Human and Machine Cognition. Available at: http://cmap.ihmc.us/Publications/ResearchPapers/TheoryUnderlyingCon ceptMaps.pdf
[2] Anderson, J. R., Byrne, M. D., Douglass, S., Lebiere, C., & Qin, Y. (2004). An integrated theory of the mind. Psychological Review, 111(4), 1036-1050.
[3] Novak, J.D. (1998) Learning, Creating, and Using Knowledge: Concept Maps as Facilitative Tools in Schools and Corporations, Lawrence Erlbaum Associates.
[4] Markham, K. M., Mintzes, J. J., & Jones, M. G. (1994). The concept map as a research and evaluation tool: Further evidence of validity. Journal of Research in Science Teaching, 31(1), 91-101.
[5] Lancaster, F. W. (1986). Vocabulary Control for Information Retrieval (2nd ed.). Arlington, VA: Information Resources Press.
[6] UMLS (2007). Unified Medical Language System. http://www.nlm.nih.gov/research/umls/
[7] Chen, H. Schatz, B., Yim, T., & Fye, D. (1995). Automatic Thesaurus Generation for an Electronic Community System. Journal of the American Society for Information Science, 46( 3), 175-193.
[8] Schatz, B.R. (1997). Information Retrieval in Digital Libraries: Bringing Search to the Net. Science, 275(5298), 327-334.
[9] Kohonen, T. (1997). Self-Organizing Maps. Springer-Verlag, Berlin.
[10] Honkela, T.; Kaski, S.; Lagus, K., Kohonen, T. (1998). Self-organizing maps of very large document collections: Justification for the WEBSOM method. In Balderjahn, I., Mathar, R., and Schader, M., editors, Classification, Data Analysis, and Data Highways, pages 245-252. Springer, Berlin.
[11] Schvaneveldt, R. W. (Ed.). (1990). Pathfinder associative networks: Studies in knowledge organization. Norwood, NJ: Ablex.
[12] Gardenfors, P. (2004). Conceptual spaces: The geometry of thought. MIT Press.
[13] Takayama, Y.; Flournoy, R.; Kaufmann, S. (1998). Information mapping: Concept-based information retrieval based on word associations. Unpublished technical report. Stanford University.
[14] Widdows, D. (2004). Geometry and meaning. CSLI Publications, Stanford University.
[15] Lin, X.; Aluker, S.; Zhu, W.; & Zhang, F. (2006). "Dynamic Concept Representation through a Visual Concept Explorer." Advances in Knowledge Organization, Vol. 10; Proceedings of the Ninth Conference of the International Society of Knowledge Organization (ISKO 2006, July 4 - 7, Vienna, Austria), pp. 367-374.
[16] Buzydlowski, J. (2002). A Comparison of Self-Organizing Maps to Pathfinder Networks for the Mapping of Author Co-Citation Analysis. (Unpublished Ph.D. dissertation, October, 2002, Drexel University).
[17] Williams, J. (2007). The Impact of Concept Map Visualizations on the Information Behavior, Perceptions of Performance, Learning and Use of Novices in the Information Retrieval Context. (Unpublished Ph.D. dissertation, May, 2007, Drexel University).